OpenMV Cam: Technical Guide on Smart Vision Camera

Introduction to OpenMV Cam
Hardware Specifications
Software and Programming
OpenMV IDE
Image Processing and Computer Vision
Interfacing with Other Devices
Example Projects
Troubleshooting and FAQs
Conclusion

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Introduction to OpenMV Cam

The OpenMV Cam is a small, low-power, and affordable machine vision camera that is designed for embedded applications. It features a powerful ARM Cortex-M7 processor, a high-resolution camera sensor, and a Python-based programming environment that makes it easy to develop computer vision applications quickly.

Some key features of the OpenMV Cam include:

ARM Cortex-M7 processor running at 216 MHz
On-board RAMand flash memory for storing programs and data
High-resolution camera sensor (up to 5 MP)
Support for a wide range of image processing and machine vision algorithms
Easy-to-use Python programming environment
Compatibility with a wide range of sensors and peripherals

Why Use OpenMV Cam?

The OpenMV Cam is an excellent choice for anyone looking to develop embedded computer vision applications quickly and easily. Some key benefits of using the OpenMV Cam include:

Low cost: The OpenMV Cam is much more affordable than many other machine vision cameras on the market, making it accessible to a wider range of users.
Easy to use: The OpenMV Cam uses a Python-based programming environment that is easy to learn and use, even for those with little programming experience.
Powerful processing: The ARM Cortex-M7 processor provides plenty of processing power for most computer vision applications.
Flexibility: The OpenMV Cam is compatible with a wide range of sensors and peripherals, making it easy to integrate into existing systems or create new ones from scratch.

Hardware Specifications

The OpenMV Cam is available in several different models, each with slightly different hardware specifications. The following table provides an overview of the key hardware specifications for each model:

Model	Resolution	Frame Rate	RAM	Flash	Interfaces
OpenMV Cam H7	5 MP	60 FPS	512 KB	2 MB	USB, I2C, SPI, UART, IO
OpenMV Cam H7 R2	5 MP	60 FPS	512 KB	2 MB	USB, I2C, SPI, UART, IO
OpenMV Cam M7	0.3 MP	60 FPS	256 KB	512 KB	USB, I2C, SPI, UART, IO
OpenMV Cam M4	0.3 MP	60 FPS	192 KB	512 KB	USB, I2C, SPI, UART, IO

As you can see, the higher-end models (H7 and H7 R2) feature a higher resolution camera sensor and more RAM and flash memory than the lower-end models (M7 and M4). All models feature a range of interfaces for connecting to other devices, including USB, I2C, SPI, UART, and general-purpose I/O pins.

Camera Sensor

The camera sensor is one of the most important components of the OpenMV Cam, as it determines the resolution and quality of the images that can be captured. The OpenMV Cam uses a high-quality CMOS image sensor that is capable of capturing images at resolutions up to 5 megapixels (2592×1944) on the H7 and H7 R2 models, and up to 0.3 megapixels (640×480) on the M7 and M4 models.

The camera sensor also supports a range of advanced features, including:

Automatic exposure control
Automatic white balance
Configurable gain and exposure time
Support for external LED illumination

These features make it easy to capture high-quality images in a variety of lighting conditions, without the need for manual adjustments.

Processor and Memory

The OpenMV Cam is powered by an ARM Cortex-M7 processor, which provides plenty of processing power for most computer vision applications. The processor runs at a clock speed of 216 MHz on the H7 and H7 R2 models, and at a slightly lower speed on the M7 and M4 models.

In addition to the processor, the OpenMV Cam also includes on-board RAM and flash memory for storing programs and data. The amount of RAM and flash memory varies depending on the model, with the H7 and H7 R2 models featuring 512 KB of RAM and 2 MB of flash memory, while the M7 and M4 models have slightly less.

Interfaces

The OpenMV Cam includes a range of interfaces for connecting to other devices and sensors. These interfaces include:

USB: The OpenMV Cam can be connected to a computer via USB for programming and data transfer.
I2C: The I2C interface allows the OpenMV Cam to communicate with a wide range of sensors and peripherals, including accelerometers, gyroscopes, and temperature sensors.
SPI: The SPI interface provides high-speed communication with other devices, such as displays and external memory.
UART: The UART interface allows the OpenMV Cam to communicate with other devices using a serial protocol.
I/O Pins: The OpenMV Cam includes a number of general-purpose I/O pins that can be used for digital input and output, analog input, and PWM output.

These interfaces make it easy to integrate the OpenMV Cam into a wide range of applications, from robotics and drones to industrial automation and security systems.

Software and Programming

One of the key benefits of the OpenMV Cam is its easy-to-use Python programming environment. Python is a high-level, interpreted programming language that is known for its simplicity and readability, making it an excellent choice for beginners and experienced programmers alike.

The OpenMV Cam uses a custom version of MicroPython, which is a lightweight implementation of Python designed for microcontrollers and embedded systems. MicroPython includes many of the same features and libraries as standard Python, but with a smaller footprint and lower resource requirements.

To program the OpenMV Cam, you will need to use the OpenMV IDE, which is a cross-platform integrated development environment that includes a code editor, terminal emulator, and image viewer. The OpenMV IDE makes it easy to write, debug, and upload code to the OpenMV Cam, as well as view and analyze images captured by the camera.

Basic Programming Concepts

Before diving into the details of programming the OpenMV Cam, it’s important to understand some basic programming concepts. These include:

Variables: Variables are used to store data in a program. In Python, variables are created using the = operator, like this: x = 10.
Data types: Python supports several data types, including integers, floating-point numbers, strings, and lists. You can specify the data type of a variable using type hints, like this: x: int = 10.
Control structures: Control structures are used to control the flow of a program. The most common control structures in Python are if statements, for loops, and while loops.
Functions: Functions are reusable blocks of code that perform a specific task. In Python, functions are defined using the def keyword, like this: def my_function():.
Libraries: Libraries are collections of pre-written code that can be used to perform common tasks. The OpenMV Cam includes several libraries for image processing, machine vision, and interfacing with sensors and peripherals.

Image Processing and Computer Vision

One of the primary uses of the OpenMV Cam is for image processing and computer vision applications. The OpenMV Cam includes several built-in libraries for performing common image processing tasks, such as:

Color tracking: The image.find_blobs() function can be used to detect and track colored objects in an image.
Face detection: The image.find_features() function can be used to detect faces in an image using Haar cascades.
QR code detection: The image.find_qrcodes() function can be used to detect and decode QR codes in an image.
Barcode detection: The image.find_barcodes() function can be used to detect and decode barcodes in an image.
Optical flow: The image.find_displacement() function can be used to track the motion of objects in an image using optical flow.

These are just a few examples of the many image processing and computer vision functions available in the OpenMV Cam libraries. By combining these functions with custom code, you can create powerful and sophisticated computer vision applications.

Interfacing with Sensors and Peripherals

In addition to image processing and computer vision, the OpenMV Cam can also be used to interface with a wide range of sensors and peripherals. This is made possible by the various interfaces available on the OpenMV Cam, including I2C, SPI, UART, and general-purpose I/O pins.

Some examples of sensors and peripherals that can be used with the OpenMV Cam include:

Accelerometers and gyroscopes: These sensors can be used to measure acceleration and orientation, which can be useful for applications such as robotics and drones.
Temperature sensors: Temperature sensors can be used to measure the ambient temperature or the temperature of a specific object or surface.
Displays: The OpenMV Cam can be used to drive a variety of displays, including OLED and TFT displays.
Motors and servos: The OpenMV Cam can be used to control motors and servos using PWM output.

To interface with these sensors and peripherals, you will need to use the appropriate libraries and functions provided by the OpenMV Cam. For example, to read data from an I2C sensor, you would use the pyb.I2C class and its associated methods.

Example Projects

To help you get started with the OpenMV Cam, here are a few example projects that demonstrate some of its capabilities:

Color Tracking

This project demonstrates how to use the OpenMV Cam to track the position of a colored object in real-time. The code uses the find_blobs() function to detect the colored object and draws a rectangle around it.

import sensor, image, time

# Initialize the camera sensor
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)

# Define the color thresholds for the object to be tracked
red_threshold = (30, 100, 15, 127, 15, 127)

while(True):
    # Capture an image
    img = sensor.snapshot()

    # Find blobs with the specified color thresholds
    for blob in img.find_blobs([red_threshold], pixels_threshold=200, area_threshold=200):
        # Draw a rectangle around the blob
        img.draw_rectangle(blob.rect(), color=(255,0,0))

    # Print the coordinates of the blob
    print(blob.cx(), blob.cy())

Face Detection

This project demonstrates how to use the OpenMV Cam to detect faces in an image using Haar cascades. The code uses the find_features() function to detect faces and draws a rectangle around each face.

import sensor, image, time

# Load the Haar cascade for face detection
face_cascade = image.HaarCascade("frontalface", stages=25)

# Initialize the camera sensor
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)

while(True):
    # Capture an image
    img = sensor.snapshot()

    # Find faces in the image
    faces = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25)

    # Draw a rectangle around each face
    for face in faces:
        img.draw_rectangle(face, color=(255,255,255))

    # Print the number of faces detected
    print(len(faces))

QR Code Scanner

This project demonstrates how to use the OpenMV Cam to detect and decode QR codes in an image. The code uses the find_qrcodes() function to detect QR codes and prints the decoded data.

import sensor, image, time

# Initialize the camera sensor
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.VGA)
sensor.set_windowing((640,480))
sensor.skip_frames(time = 2000)

while(True):
    # Capture an image
    img = sensor.snapshot()

    # Find QR codes in the image
    qrcodes = img.find_qrcodes()

    # Draw a rectangle around each QR code and print the decoded data
    for qrcode in qrcodes:
        img.draw_rectangle(qrcode.rect(), color=(255,0,0))
        print(qrcode.payload())

These are just a few examples of the many projects that can be built using the OpenMV Cam. With its powerful processor, high-resolution camera, and easy-to-use Python programming environment, the OpenMV Cam is an excellent tool for anyone interested in computer vision and embedded systems.

Troubleshooting and FAQs

FAQ

What is the maximum resolution of the OpenMV Cam?
The maximum resolution of the OpenMV Cam depends on the model. The H7 and H7 R2 models have a maximum resolution of 5 megapixels (2592×1944), while the M7 and M4 models have a maximum resolution of 0.3 megapixels (640×480).
Can the OpenMV Cam be used for real-time video processing?
Yes, the OpenMV Cam is capable of real-time video processing at frame rates up to 60 FPS, depending on the resolution and complexity of the image processing algorithms being used.
What programming language is used to program the OpenMV Cam?
The OpenMV Cam is programmed using a custom version of MicroPython, which is a lightweight implementation of Python designed for microcontrollers and embedded systems.
Can the OpenMV Cam be used with other programming languages besides Python?
While the OpenMV Cam is primarily designed to be used with MicroPython, it is possible to use other programming languages with the camera, such as C/C++, by using the OpenMV Cam’s firmware API.
What kind of applications can the OpenMV Cam be used for?
The OpenMV Cam can be used for a wide range of computer vision and image processing applications, including object tracking, face detection, barcode and QR code scanning, optical character recognition, and more. It can also be used for robotics, drones, industrial automation, and security systems, among other applications.

Troubleshooting

The OpenMV Cam is not recognized by my computer.
Make sure that the OpenMV Cam is properly connected to your computer via USB and that the correct drivers are installed. If necessary, try using a different USB cable or port.
I am getting an error message when trying to upload code to the OpenMV Cam.
Double-check that your code is free of syntax errors and that you are using the correct version of the OpenMV IDE and firmware for your camera model. If the problem persists, try reinstalling the OpenMV IDE and firmware.
The image quality from the OpenMV Cam is poor.
Make sure that the camera lens is clean and free of debris. Adjust the camera settings, such as exposure time and gain, to optimize image quality for your particular lighting conditions. If necessary, consider using an external light source to improve image quality.
**The OpenMV Cam is running slowly